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Modulating signal recognition method based on complexity feature under low signal to noise ratio

A technology of complex characteristics and modulated signals, which is applied in the direction of modulation type identification, modulated carrier system, digital transmission system, etc., and can solve problems such as classification and identification of modulation types that are difficult to communicate with

Inactive Publication Date: 2015-07-22
SHANGHAI DIANJI UNIV
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AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to provide a modulation signal identification method based on complexity features under low signal-to-noise ratio, which can overcome the problem that it is difficult to classify and identify the modulation type of communication signals under low signal-to-noise ratio in existing identification methods

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  • Modulating signal recognition method based on complexity feature under low signal to noise ratio
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  • Modulating signal recognition method based on complexity feature under low signal to noise ratio

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Embodiment Construction

[0046] In order to make the above objects, features and advantages of the present invention more comprehensible, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0047] like figure 1 and 2 As shown, the present invention provides a modulation signal identification method based on complexity features under a low signal-to-noise ratio, including:

[0048] Step S1, first perform discrete preprocessing on the received unknown communication modulation signal to obtain a discrete signal sequence:

[0049] The received communication modulation signal is s, and the preprocessed discrete signal sequence is {s(i)}, where s(i) is the i-th sampling point value of the discrete signal sequence, i=1,2,..., N 0 Indicates the number of sampling points of the signal, N 0 is the length of the signal sequence;

[0050] Step S2, recombining the discrete signal sequence (discrete signal point recombination...

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Abstract

The invention provides a modulating signal recognition method based on a complexity feature under low signal to noise ratio. The method comprises the following steps: extracting multi-fractal dimension features of different communication modulating signals, protruding features of different probability points in a time signal sequence so as to extract the features of different communication modulating signal types; furthermore, grouping pre-processed discrete-time signal sequences so as to simplify the computation of the multi-fractal dimension on the one hand, and translate the long time signal sequence into small sequence sections to observe and compute on the other hand, thereby performing small-range feature expression on the signal, and extracting the feature of the signal in a more refined manner; moreover, performing grey correlation processing on the extracted multi-fractal dimension feature of the unknown communication modulating signal and the computed multi-fractal dimension feature of the known communication modulating signal in a database, selecting the modulating type of the signal with large correlation degree as the modulating type of the unknown communication modulating signal, and then realizing the classification recognition of the modulating type.

Description

technical field [0001] The invention relates to a modulation signal identification method based on complexity features under low signal-to-noise ratio. Background technique [0002] Communication signal modulation recognition technology is an important content in software radio and other fields. Its research focuses on intercepting or analyzing communication signals transmitted by communication stations in the absence of prior information, complex environments and noise interference to identify signal The information such as the modulation type and modulation parameters can provide a basis for further analysis and processing. At present, the existing communication signal modulation recognition methods include step-by-step classification recognition algorithms based on parameters such as signal instantaneous amplitude, instantaneous frequency and instantaneous phase, and spectral symmetry; digital modulation signal recognition algorithms based on spectral correlation function...

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Application Information

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IPC IPC(8): H04L27/00
CPCH04L27/0012
Inventor 李靖超胡兰馨应雨龙李骜董春蕾郭健史婉莹
Owner SHANGHAI DIANJI UNIV
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